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Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors

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  • Olanrewaju, O.A.
  • Jimoh, A.A.
  • Kholopane, P.A.

Abstract

This paper puts forward an integrated approach, based on logarithmic mean divisia index (LMDI) – an index decomposition analysis (IDA) method, an artificial neural network (ANN) and a data envelopment analysis (DEA) for the analysis of total energy efficiency and optimization in an industrial sector. The energy efficiency assessment and the optimization of the proposed model use LMDI to decompose energy consumption into activity, structural and intensity indicators, which serve as inputs to the ANN. The ANN model is verified and validated by performing a linear regression comparison between the specifically measured energy consumption and the corresponding predicted energy consumption. The proposed approach utilizes the measure-specific, super-efficient DEA model for sensitivity analysis to determine the critical measured energy consumption and its optimization reductions. The proposed method is validated by its application to determine the efficiency computation and an analysis of historical data as well as the prediction and optimization capability of the Canadian industrial sector.

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  • Olanrewaju, O.A. & Jimoh, A.A. & Kholopane, P.A., 2012. "Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors," Energy, Elsevier, vol. 46(1), pages 629-635.
    • Handle: RePEc:eee:energy:v:46:y:2012:i:1:p:629-635
    DOI: 10.1016/j.energy.2012.07.037
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    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Sozen, Adnan & Arcaklioglu, Erol, 2007. "Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey," Energy Policy, Elsevier, vol. 35(10), pages 4981-4992, October.
    3. González, P.Fernández & Suárez, R.Pérez, 2003. "Decomposing the variation of aggregate electricity intensity in Spanish industry," Energy, Elsevier, vol. 28(2), pages 171-184.
    4. Choi, Ki-Hong & Ang, B.W. & Ro, K.K., 1995. "Decomposition of the energy-intensity index with application for the Korean manufacturing industry," Energy, Elsevier, vol. 20(9), pages 835-842.
    5. Oladiran, M.T. & Meyer, J.P., 2007. "Energy and exergy analyses of energy consumptions in the industrial sector in South Africa," Applied Energy, Elsevier, vol. 84(10), pages 1056-1067, October.
    6. Liu, C.H. & Lin, Sue J. & Lewis, Charles, 2010. "Evaluation of thermal power plant operational performance in Taiwan by data envelopment analysis," Energy Policy, Elsevier, vol. 38(2), pages 1049-1058, February.
    7. Mendiluce, María & Pérez-Arriaga, Ignacio & Ocaña, Carlos, 2010. "Comparison of the evolution of energy intensity in Spain and in the EU15. Why is Spain different?," Energy Policy, Elsevier, vol. 38(1), pages 639-645, January.
    8. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    9. Hu, Jin-Li & Kao, Chih-Hung, 2007. "Efficient energy-saving targets for APEC economies," Energy Policy, Elsevier, vol. 35(1), pages 373-382, January.
    10. Ozawa, Leticia & Sheinbaum, Claudia & Martin, Nathan & Worrell, Ernst & Price, Lynn, 2002. "Energy use and CO2 emissions in Mexico's iron and steel industry," Energy, Elsevier, vol. 27(3), pages 225-239.
    11. Sozen, Adnan & Gulseven, Zafer & Arcaklioglu, Erol, 2007. "Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies," Energy Policy, Elsevier, vol. 35(12), pages 6491-6505, December.
    12. Inglesi-Lotz, R. & Pouris, A., 2012. "Energy efficiency in South Africa: A decomposition exercise," Energy, Elsevier, vol. 42(1), pages 113-120.
    13. Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    14. Ang, B.W. & Zhang, F.Q., 2000. "A survey of index decomposition analysis in energy and environmental studies," Energy, Elsevier, vol. 25(12), pages 1149-1176.
    15. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
    16. Hoekstra, Rutger & van den Bergh, Jeroen C. J. M., 2003. "Comparing structural decomposition analysis and index," Energy Economics, Elsevier, vol. 25(1), pages 39-64, January.
    17. Safa, M. & Samarasinghe, S., 2011. "Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”," Energy, Elsevier, vol. 36(8), pages 5140-5147.
    18. Abdelaziz, E.A. & Saidur, R. & Mekhilef, S., 2011. "A review on energy saving strategies in industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 150-168, January.
    19. Salta, Myrsine & Polatidis, Heracles & Haralambopoulos, Dias, 2009. "Energy use in the Greek manufacturing sector: A methodological framework based on physical indicators with aggregation and decomposition analysis," Energy, Elsevier, vol. 34(1), pages 90-111.
    20. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    21. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    Full references (including those not matched with items on IDEAS)

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